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A. Rossi, Pedreschi, D., Clifton, D. A., and Morelli, D., “Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts”, Sensors, vol. 20, p. 7122, 2020.
S. Ruggieri, “Introduction to the special issue on Artificial Intelligence for Society and Economy”, Intelligenza Artificiale, vol. 9, pp. 23–23, 2015.
S. Ruggieri and Mesnard, F., “Typing Linear Constraints for Moding CLP() Programs”, in SAS, 2008, pp. 128-143.
S. Ruggieri, Hajian, S., Kamiran, F., and Zhang, X., “Anti-discrimination analysis using privacy attack strategies”, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2014.
S. Ruggieri, “Using t-closeness anonymity to control for non-discrimination.”, Trans. Data Privacy, vol. 7, pp. 99–129, 2014.
S. Ruggieri, Eirinakis, P., Subramani, K., and Wojciechowski, P., “On the complexity of quantified linear systems”, Theoretical Computer Science, vol. 518, pp. 128–134, 2014.
S. Ruggieri, “Data Anonymity Meets Non-discrimination”, in Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on, 2013.
S. Ruggieri, “Learning from polyhedral sets”, in Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013.
S. Ruggieri and Turini, F., “A KDD process for discrimination discovery”, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2016.
S. Ruggieri, “Enumerating Distinct Decision Trees”, in International Conference on Machine Learning, 2017.
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R. Portocarre Sarmento, Lemos, L., Cordeiro, M., Rossetti, G., and Cardoso, D., “DynComm R Package–Dynamic Community Detection for Evolving Networks”, arXiv preprint arXiv:1905.01498, 2019.
V. D. P. Servedio, Caminiti, S., Gravino, P., Loreto, V., Sirbu, A., and Tria, F., “Large Scale Engagement Through Web-Gaming and Social Computations”, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 237–254.
M. Setzu, Guidotti, R., Monreale, A., and Turini, F., “Global Explanations with Local Scoring”, in Machine Learning and Knowledge Discovery in Databases, Cham, 2020.
M. Setzu and Atzori, M., “SPARQL Queries over Source Code”, in 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), 2016.
M. Setzu, Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., and Giannotti, F., “GLocalX - From Local to Global Explanations of Black Box AI Models”, vol. 294, p. 103457, 2021.
A. Sirbu and Babaoglu, O., “Towards Data-Driven Autonomics in Data Centers”, in IEEE International Conference on Cloud and Autonomic Computing, 2015.
A. Sirbu, Crane, M., and Ruskin, H. J., “Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks”, Microarrays, vol. 4, pp. 255–269, 2015.
A. Sirbu, Pedreschi, D., Giannotti, F., and Kertész, J., “Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model”, PloS one, vol. 14, p. e0213246, 2019.
A. Sirbu, Giannotti, F., Pedreschi, D., and Kertész, J., “Public opinion and Algorithmic bias”, ERCIM News, 2019.
A. Sirbu and Babaoglu, O., “Power Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer”, 22nd International European Conference on Parallel and Distributed Computing, Euro-Par 2016, vol. LNCS 9833. Springer LNCS, Grenoble, France, 2016.
A. Sirbu, Crane, M., and Ruskin, H. J., “EGIA–Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach”, in Complex Networks V, Springer International Publishing, 2014, pp. 217–229.
A. Sirbu, Loreto, V., Servedio, V. D. P., and Tria, F., “Cohesion, consensus and extreme information in opinion dynamics”, Advances in Complex Systems, vol. 16, p. 1350035, 2013.
A. Sirbu, Ruskin, H. J., and Crane, M., “Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role”, in Evolutionary Algorithms, InTech, 2011.
A. Sirbu, Ruskin, H. J., and Crane, M., “Regulatory network modelling: Correlation for structure and parameter optimisation”, Proceedings of The IASTED Technology Conferences (International Conference on Computational Bioscience), Cambridge, Massachusetts, pp. 3473–3481, 2010.
A. Sirbu, Loreto, V., Servedio, V. D. P., and Tria, F., “Opinion dynamics with disagreement and modulated information”, Journal of Statistical Physics, pp. 1–20, 2013.

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